在代码中用httpclient.InferenceServerClient()函数创建 HTTP 的 triton_client 对象时,需要给定“concurrnecy(并发数量)”参数,而创建 gRPC 的用户端时就不需要这个参数。 调用异步模式有时会需要搭配数据流(stream)的处理器(handle),因此在实际推理的函数就有triton_client.async_infer()与triton_client.async_stream...
在代码中用httpclient.InferenceServerClient()函数创建 HTTP 的 triton_client 对象时,需要给定“concurrnecy(并发数量)”参数,而创建 gRPC 的用户端时就不需要这个参数。 调用异步模式有时会需要搭配数据流(stream)的处理器(handle),因此在实际推理的函数就有triton_client.async_infer()与triton_client.async_stream...
For C++ client, seecompression_algorithmparameter in theInfer,AsyncInferandStartStreamfunctions ingrpc_client.h. By default, the parameter is set asGRPC_COMPRESS_NONE. Similarly, for Python client, seecompression_algorithmparameter ininfer,async_inferandstart_streamfunctions ingrpc/__init__.py. TheC...
stdout = output_catcher triton_client.async_stream_infer(model_name, inputs) # Send request #Wait for server to close the stream triton_client.stop_stream() # 恢复标准输出 sys.stdout = sys.__stdout__ # Parse the responses response_text = [] while True: try: result = ...
triton_client.async_infer( FLAGS.model_name, inputs, partial(completion_callback, user_data), request_id=str(sent_count), model_version=FLAGS.model_version, outputs=outputs) else: async_requests.append( triton_client.async_infer( FLAGS.model_name, ...
The provided client libraries are: C++ and Python APIsthat make it easy to communicate with Triton from your C++ or Python application. Using these libraries you can send either HTTP/REST or GRPC requests to Triton to access all its capabilities: inferencing, status and health, statistics and ...
InferenceServerClient(url=url, verbose=False, concurrency=32) as client: ... # Hit triton server n_requests = 4 responses = [] for i in range(n_requests): responses.append(client.async_infer(model_name, model_version=model_version, inputs=inputs, outputs=outputs)) with grpc: client =...
autoclient = triton::client::TritonClientGrpcAsync::create(argv[1], argv[2]); //获取模型元数据 std::shared_ptr<triton::client::ModelMetadata> model_metadata; std::stringmodel_name ="your_model_name"; std::stringversion ="your_model_version"; std::stringresponse; std::stringerror; if(...
import tritonclient.http as httpclient if __name__ == '__main__': triton_client = httpclient.InferenceServerClient(url='127.0.0.1:8000') inputs = [] inputs.append(httpclient.InferInput('INPUT0', [4], "FP32")) inputs.append(httpclient.InferInput('INPUT1', [4], "FP32")) ...
(tritonhttpclient.InferResult method) get_raw_handle() (in module tritonshmutils.cuda_shared_memory) get_response() (tritongrpcclient.InferResult method) (tritonhttpclient.InferResult method) get_result() (tritonhttpclient.InferAsyncRequest method) ...